论文标题
使用内域知识的培训一般表示以遥感
Training general representations for remote sensing using in-domain knowledge
论文作者
论文摘要
自动找到良好的一般遥感表示形式可以在广泛的应用程序上执行转移学习 - 提高准确性并减少所需数量的培训样本。本文调查了通用遥感表示形式的开发，并探讨了哪些特征对于数据集成为代表学习的良好来源很重要。对于此分析，选择了五个不同的遥感数据集，并将其用于两者不相交的上游表示学习以及下游模型培训和评估。常见的评估协议用于建立这些数据集的基准，以实现最先进的性能。结果表明，尤其是在可用训练样本数量少的情况下，与从头开始的训练模型相比，可以观察到显着的性能提高，或者仅在ImageNet上进行微调（分别在100个培训样本下，分别高达11％和40％）。所有数据集和预处理的表示模型均在线发布。
Automatically finding good and general remote sensing representations allows to perform transfer learning on a wide range of applications - improving the accuracy and reducing the required number of training samples. This paper investigates development of generic remote sensing representations, and explores which characteristics are important for a dataset to be a good source for representation learning. For this analysis, five diverse remote sensing datasets are selected and used for both, disjoint upstream representation learning and downstream model training and evaluation. A common evaluation protocol is used to establish baselines for these datasets that achieve state-of-the-art performance. As the results indicate, especially with a low number of available training samples a significant performance enhancement can be observed when including additionally in-domain data in comparison to training models from scratch or fine-tuning only on ImageNet (up to 11% and 40%, respectively, at 100 training samples). All datasets and pretrained representation models are published online.
